Applying machine learning to catalogue matching in astrophysics

We present the results of applying automated machine learning techniques to the problem of matching different object catalogues in astrophysics. In this study, we take two partially matched catalogues where one of the two catalogues has a large positional uncertainty. The two catalogues we used here were taken from the H I Parkes All Sky Survey (HIPASS) and SuperCOSMOS optical survey. Previous work had matched 44 per cent (1887 objects) of HIPASS to the SuperCOSMOS catalogue. A supervised learning algorithm was then applied to construct a model of the matched portion of our catalogue. Validation of the model shows that we achieved a good classification performance (99.12 per cent correct). Applying this model to the unmatched portion of the catalogue found 1209 new matches. This increases the catalogue size from 1887 matched objects to 3096. The combination of these procedures yields a catalogue that is 72 per cent matched.

Matching of catalogues by probabilistic pattern classification

We consider the statistical problem of catalogue matching from a machine learning perspective with the goal of producing probabilistic outputs, and using all available information. A framework is provided that unifies two existing approaches to producing probabilistic outputs in the literature, one based on combining distribution estimates and the other based on combining probabilistic classifiers. We apply both of these to the problem of matching the HI Parkes All Sky Survey radio catalogue with large positional uncertainties to the much denser SuperCOSMOS catalogue with much smaller positional uncertainties. We demonstrate the utility of probabilistic outputs by a controllable completeness and efficiency trade-off and by identifying objects that have high probability of being rare. Finally, possible biasing effects in the output of these classifiers are also highlighted and discussed.

Modelling the Human Values Scale from Consumers Transactional Data Bases

The main objective of this paper aims at developing a methodology that takes into account the human factor extracted from the data base used by the recommender systems, and which allow to resolve the specific problems of prediction and recommendation. In this work, we propose to extract the user's human values scale from the data base of the users, to improve their suitability in open environments, such as the recommender systems. For this purpose, the methodology is applied with the data of the user after interacting with the system. The methodology is exemplified with a case study

Evaluation of CARBIB and CARCAT data bases

La evaluacion de las bases de datos CARBIB y CARCAT cubre areas tales como: formatos de intercambio, compatibilidad, cobertura tematica y geografica, tipo de documento a ingresar en relacion con su origen; y valor potencial para la region.

A NEURAL NET FOR EXTRACTING KNOWLEDGE FROM NATURAL-LANGUAGE DATA-BASES

The present paper introduces a new model of fuzzy neuron, one which increases the computational power of the artificial neuron, turning it also into a symbolic processing device. This model proposes the synapsis to be symbolically and numerically defined, by means of the assignment of tokens to the presynaptic and postsynaptic neurons. The matching or concatenation compatibility between these tokens is used to decided about the possible connections among neurons of a given net. The strength of the compatible synapsis is made dependent on the amount of the available presynaptic and post synaptic tokens. The symbolic and numeric processing capacity of the new fuzzy neuron is used here to build a neural net (JARGON) to disclose the existing knowledge in natural language data bases such as medical files, set of interviews, and reports about engineering operations.

Sequence tag identification of intact proteins by matching tanden mass spectral data against sequence data bases.

Molecular and fragment ion data of intact 8- to 43-kDa proteins from electrospray Fourier-transform tandem mass spectrometry are matched against the corresponding data in sequence data bases. Extending the sequence tag concept of Mann and Wilm for matching peptides, a partial amino acid sequence in the unknown is first identified from the mass differences of a series of fragment ions, and the mass position of this sequence is defined from molecular weight and the fragment ion masses. For three studied proteins, a single sequence tag retrieved only the correct protein from the data base; a fourth protein required the input of two sequence tags. However, three of the data base proteins differed by having an extra methionine or by missing an acetyl or heme substitution. The positions of these modifications in the protein examined were greatly restricted by the mass differences of its molecular and fragment ions versus those of the data base. To characterize the primary structure of an unknown represented in the data base, this method is fast and specific and does not require prior enzymatic or chemical degradation.

Global compilation of chlorophyll fluorescence profiles from several data bases, and from published and unpublished individual sources

The present data set includes 268,127 vertical in situ fluorescence profiles obtained from several available online databases and from published and unpublished individual sources. Metadata about each profiles are given in the file provided here in further details. The majority of profiles comes from the National Oceanographic Data Center (NODC) and the fluorescence profiles acquired by Bio-Argo floats available on the Oceanographic Autonomous Observations (OAO) platform (63.7% and 12.5% respectively). Different modes of acquisition were used to collect the data presented in this study: (1) CTD profiles are acquired using a fluorometer mounted on a CTD-rosette; (2) OSD (Ocean Station Data) profiles are derived from water samples and are defined as low resolution profiles; (3) the UOR (Undulating Oceanographic Recorder) profiles are acquired by a equipped with a fluorometer and towed by a research vessel; (4) PA profiles are acquired by autonomous platforms (here profiling floats or elephant seals equipped with a fluorometer). Data acquired from gliders are not included in the compilation.

Accessing federal data bases for contaminated site clean-up technologies /

"May 1991"

Improvements in transportation fire safety data bases. Final report.

Transportation Department, Office of the Assistant Secretary for Policy and International Affairs, Washington, D.C.

Commonalities in transportation fire safety: regulations, research and development, and data bases. Final report.

Transportation Department, Office of Environment and Safety, Washington, D.C.

Feasibility of developing training programs designed to improve deficient driver factors. Volume I: a review of existing data bases and recommendations for future data collection. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Natural resources data bases [microform] : hearing before the Subcommittee on Department Operations, Research, and Foreign Agriculture of the Committee on Agriculture, House of Representatives, Ninety-seventh Congress, first session, June 2, 1981.

"Serial no. 97-K."

Generalized organization of large data-bases : a set-theoretic approach to relations /

Mode of access: Internet.

Inventory of data bases, graphics packages, and models in Department of Energy laboratories /

"ORNL/EIS-144."